Available vehicle parking space detection using machine learning

ABSTRACT

A system includes a processor and a memory storing instructions that, when executed by the processor cause the system to generate a machine learning model; generate an artificial neural network; analyze an image of a parking area using a spot detection machine learning model; analyze the image of the parking area using a vehicle detection machine learning model; and classify a parking space as available when an area of intersection does not exceed a predetermined value. A method includes analyzing an image of a parking area using a first machine learning model; analyzing the image of the parking area using second machine learning model; and classifying a parking space as available when an area of intersection does not exceed a predetermined value. A method includes generating a spot detection machine learning model; and generating, by analyzing a plurality of labeled images, an artificial neural network.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No. 16/823,736, filed Mar. 19, 2020, and entitled “AVAILABLE VEHICLE PARKING SPACE DETECTION USING MACHINE LEARNING,” which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure is generally directed to methods and systems for performing building automation, and more particularly, machine learning techniques for automated parking space detection.

BACKGROUND

Building management is complicated, as buildings conventionally lack automation. As a result, building owners see significant costs associated with a variety of problems relating to the physical infrastructure of the building. For example, visitors who are unfamiliar with the building may have difficulty locating building tenants. Visitors and tenants have difficulty locating parking spots (e.g., for electric vehicles) and finding office locations (e.g., conference rooms). Tenants may leave electric lighting on at times when rooms are unoccupied, relating in higher costs and wasting power. Further, conventional building security is not sufficient.

Technology-focused companies increasingly deploy communications platforms (e.g., Slack, Microsoft Teams, Cisco Webex, etc.) in their organizations, and users are increasingly integrating such platforms into their daily workflows. However, conventional communications platforms lack building management functionality.

BRIEF SUMMARY

In one aspect, a computing system for classifying a vehicle parking space includes one or more processors; and a memory storing instructions. When the instructions are executed by the one or more processors, the instructions cause the system to (i) generate a spot detection machine learning model trained to identify a parking space bounding box corresponding to the vehicle parking space, the spot detection machine learning model including a contour approximation algorithm; (ii) generate, by analyzing a plurality of labeled images, an artificial neural network trained to identify a vehicle bounding box; (iii) analyze an image of a parking area using the trained spot detection machine learning model to identify the parking space bounding box; (iv) analyze the image of the parking area using the trained vehicle detection machine learning model to identify the vehicle bounding box; and (v) classify the vehicle parking space as available when an area of intersection of the parking space bounding box and the vehicle bounding box does not exceed a predetermined value.

In another aspect a computer-implemented method for classifying a vehicle parking space includes: (i) analyzing an image of a parking area using a spot detection machine learning model to identify a parking space bounding box; (ii) analyzing the image of the parking area using a vehicle detection machine learning model to identify a vehicle bounding box; and (iii) classifying the vehicle parking space as available when an area of intersection of the parking space bounding box and the vehicle bounding box does not exceed a predetermined value.

In yet another aspect, a computer-implemented method for training a plurality of machine learning models used for classifying a vehicle parking space includes: (i) generating a spot detection machine learning model trained to identify a parking space bounding box corresponding to the vehicle parking space, the spot detection machine learning model including a contour approximation algorithm; and (ii) generating, by analyzing a plurality of labeled images, an artificial neural network trained to identify a vehicle bounding box.

BRIEF DESCRIPTION OF THE FIGURES

The figures described below depict various aspects of the system and methods disclosed therein. It should be understood that each figure depicts one embodiment of a particular aspect of the disclosed system and methods, and that each of the figures is intended to accord with a possible embodiment thereof. Further, wherever possible, the following description refers to the reference numerals included in the following figures, in which features depicted in multiple figures are designated with consistent reference numerals.

FIG. 1 depicts an exemplary computing environment in which methods and systems for performing building automation may be implemented, according to one embodiment.

FIG. 2A depicts an exemplary building automation graphical user interface web application, according to one embodiment.

FIG. 2B depicts an exemplary building automation graphical user interface bot viewer, according to one embodiment.

FIG. 3A depicts exemplary building automation e-receptionist data, according to one embodiment.

FIG. 3B depicts an exemplary building automation e-receptionist login graphical user interface, according to one embodiment.

FIG. 3C depicts an exemplary building automation e-receptionist registration graphical user interface, according to one embodiment.

FIG. 3D depicts an exemplary building automation e-receptionist photo registration graphical user interface, according to one embodiment.

FIG. 3E depicts an exemplary building automation e-receptionist registration submission graphical user interface, according to one embodiment.

FIG. 3F depicts an exemplary building automation bot notification graphical user interface, according to one embodiment.

FIG. 3G depicts an exemplary building automation email notification graphical user interface, according to one embodiment.

FIG. 3H depicts exemplary building automation registration log data, according to one embodiment.

FIG. 4A depicts exemplary building automation motion detection notification, according to one embodiment.

FIG. 4B depicts exemplary building automation badge registration graphical user interface, according to one embodiment.

FIG. 4C depicts an exemplary computing system for implementing multi-factor badging, according to one embodiment.

FIG. 4D depicts an exemplary multi-factor prompt graphical user interface, according to one embodiment.

FIG. 4E depicts an exemplary multi-factor prompt graphical user interface, according to one embodiment.

FIG. 4F depicts an exemplary multi-factor prompt graphical user interface, according to one embodiment.

FIG. 4G depicts an exemplary multi-factor prompt graphical user interface, according to one embodiment.

FIG. 5A depicts an exemplary bot command graphical user interface, according to one embodiment.

FIG. 5B depicts an exemplary bot command graphical user interface for map querying, according to one embodiment.

FIG. 5C depicts an exemplary map graphical user interface for displaying map query results, according to one embodiment.

FIG. 5D depicts an exemplary bot command graphical user interface for map querying, according to one embodiment.

FIG. 5E depicts an exemplary mixed-use parking lot, according to one embodiment.

FIG. 5F depicts an exemplary bot command graphical user interface for parking querying, according to one embodiment.

FIG. 5G depicts an exemplary bot command graphical user interface for parking querying, according to one embodiment.

FIG. 6A depicts exemplary data logs for tracking room presence, according to an embodiment.

FIG. 6B depicts an exemplary room presence graphical user interface, according to an embodiment.

FIG. 7A depicts an exemplary lighting control graphical user interface, according to an embodiment.

FIG. 7B depicts an exemplary lighting control graphical user interface, according to an embodiment.

FIG. 8 depicts an exemplary computer-implemented method for performing building automation, according to an embodiment.

The figures depict preferred embodiments for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the systems and methods illustrated herein may be employed without departing from the principles of the invention described herein.

DETAILED DESCRIPTION Overview

The present techniques provide methods and systems for, inter alia, managing aspects of buildings (e.g., an office building). For example, the present techniques include aspects of building management such as visitor registration/reception, security badging, parking spot identification/allocation (e.g., for an electric vehicle), room presence techniques, lighting control techniques, and administration and reporting techniques. The present techniques improve upon conventional buildings by adding automated technological capabilities to enhance building management.

The present techniques may utilize machine learning (ML) technologies, computer vision (CV) and/or artificial intelligence (AI) to create a modern automated workplace. For example, in some embodiments, the present techniques provide automated visitor check-in, notifications, three-factor door security, electric vehicle (EV) parking spot locating, conference room light controls, conference room delegation, and reporting in one or more graphical user interface (GUI). Some embodiments include a bot interface and/or backend module.

The present techniques leverage modern technology to automate mundane tasks associated with operating an office building. For example, the present techniques eliminate the need for building personnel to turn lights off at night, to adjust heating and cooling, and/or to ensure that doors are locked. Further, a receptionist is no longer needed to check in a visitor or to locate an available conference room. The present techniques may determine the status of parking spaces in real time. By automating building management tasks, building owners will see significant cost and resource savings, building occupants may locate an EV parking spot or find a conference room, and visitors may seamlessly enter the building environment. Building security is significantly improved by adding multi-factor authentication that may be configured to use more or fewer factors depending on the desired security level of the room or area. The present techniques enable visitor badge management and automated kiosk-based check in, and automatic rule-based conference room allocation.

The present techniques improve over conventional chat platforms, which lack any building automation functionality, by providing users with rich functionality for accessing and/or controlling important aspects of building management (e.g., access to office information, control of room lighting, etc.) using an interface (e.g., a chat bot) that users are already familiar with. Additional benefits over conventional techniques include simple to use interfaces that provide quick and easy access to building information for building administrators, employees and customers. By automating tasks, the present techniques provide a uniform user experience while improving consistency and reducing errors. The present techniques reimagine how important aspects of office buildings are managed using modern technologies.

Exemplary Computing Environment

FIG. 1 depicts an exemplary computing environment 100 in which the techniques disclosed herein may be implemented, according to an embodiment. The environment 100 includes a client computing device 102, a server 104, and a network 106. Some embodiments may include a plurality of client computing devices 102 and/or a plurality of servers 104.

The client computing device 102 may be an individual server, a group (e.g., cluster) of multiple servers, or another suitable type of computing device or system (e.g., a collection of computing resources). For example, the client computing device 102 may be any suitable computing device (e.g., a server, a mobile computing device, a smart phone, a tablet, a laptop, a wearable device, etc.). In some embodiments, one or more components of the computing device 102 may be embodied by one or more virtual instances (e.g., a cloud-based virtualization service). In such cases, one or more client computing device 102 may be included in a remote data center (e.g., a cloud computing environment, a public cloud, a private cloud, etc.).

In some embodiments, as discussed with respect to FIG. 4C, the computing environment 100 may include one or more embedded computing devices (e.g., one or more Raspberry Pi single-board computers). For example, an embedded computing device may be installed in a security system, such as a three-factor authentication system. The environment 100 may include more or fewer components. For example, in some embodiments, the environment 100 may include one or more smart light bulbs, one or more switches, one or more power supplies, one or more LED screens, one or more cameras, one or more EV charging stations, etc.

The network 106 may be a single communication network, or may include multiple communication networks of one or more types (e.g., one or more wired and/or wireless local area networks (LANs), and/or one or more wired and/or wireless wide area networks (WANs) such as the Internet). The network 106 may enable bidirectional communication between the client computing device 102 and the server 104, and/or between multiple client computing devices 102, for example.

The client computing device 102 includes a processor 110 and a network interface controller (NIC) 112. The processor 110 may include any suitable number of processors and/or processor types, such as CPUs and one or more graphics processing units (GPUs). Generally, the processor 110 is configured to execute software instructions stored in a memory 114. The memory 114 may include one or more persistent memories (e.g., a hard drive/solid state memory) and stores one or more set of computer executable instructions/modules 120, including a graphical user interface (GUI) module 122, and a bot client module 124.

The client computing device 102 further includes an input device 140 and an output device 142. The input device 140 may include any suitable device or devices for receiving input, such as one or more microphone, one or more camera, a hardware keyboard, a hardware mouse, a capacitive touch screen, etc. The output device 142 may include any suitable device for conveying output, such as a hardware speaker, a computer monitor, a touch screen, etc. In some cases, the input device 140 and the output device 142 may be integrated into a single device, such as a touch screen device that accepts user input and displays output. The client computing device 102 may be associated with (e.g., owned/operated by) a company that services enterprise customers.

The NIC 112 may include any suitable network interface controller(s), such as wired/wireless controllers (e.g., Ethernet controllers), and facilitate bidirectional/multiplexed networking over the network 106 between the client computing device 102 and other components of the environment 100 (e.g., another client computing device 102, the server 104, an electronic database, etc.).

The one or more modules 120 implement specific functionality. For example, in an embodiment, the GUI module 122 includes computer-executable instructions that, when executed, cause a computer to display information (e.g., a web page, a computing application, data, etc) on a computing screen (e.g., via the output device 142). In an embodiment, the GUI module 122 receives input from the input device 140. For example, the GUI module 122 may receive an image via a camera input device 140. The GUI module 122 may receive typed information via a touch screen or keyboard input device 140. The GUI module 122 may include instructions for storing input (e.g., in the memory 120). The GUI module 122 may include instructions for transmitting the input via the network 106.

The bot client module 124 may store instructions for accessing a remote bot. The remote bot may accept commands and perform other functions, as discussed below. The bot client module 124 may convert user input into bot commands that the remote bot is programmed to execute.

The server 104 includes a processor 150 and a network interface controller (NIC) 152. The server 104 may further include a database 190. The database 190 may be a structured query language (SQL) database (e.g., a MySQL database, an Oracle database, etc.) or another type of database (e.g., a not only SQL (NoSQL) database). The server 104 may include a library of client bindings for accessing the database 190. In some embodiments, the database 190 is located remote from the server 104. For example, the database 190 may be implemented using a RESTdb.IO database, in some embodiments.

The processor 110 may include any suitable number of processors and/or processor types, such as CPUs and one or more graphics processing units (GPUs). Generally, the processor 150 is configured to execute software instructions stored in a memory 154. The memory 154 may include one or more persistent memories (e.g., a hard drive solid state memory) and stores one or more set of computer executable instructions/modules 160, including a bot module 162, an e-reception module 164, a badging module 166, a parking module 168, a room module 170, a lighting module 172, an admin module 174, a reporting module 176, and a machine learning module 178. Each of the modules 160 implements specific functionality related to the present techniques.

In some embodiments, a plurality of the modules 160 may implement a particular technique. For example, the e-reception module 164 and the badging module 166 may, together, authenticate a visiting user and a visited user. The one or more modules 160 FIG. 1 will now be described with respect to a plurality of GUI applications and exemplary embodiments.

Exemplary Building Management Suite Web Application

The functionality facilitated by the one or more modules 160 may be accessible via a networked application, in some embodiments. For example, FIG. 2A depicts an exemplary building automation GUI 200 including an application 202. The application 202 may be a web browser. The browser may correspond to a set of computer-executable instructions stored in the memory of a client application, such as the client computing device 102 of FIG. 1 . The user may access a menu of available functions 204 that include multiple administrative links. In general, the application 202 is accessed by an administrative user of the company. The user may be required to authenticate before the application 202 allows the user to access the links within the menu 204. The application 202 may a web application served to the user by, for example, the admin module 174 of FIG. 1 , via the network 106. The present techniques may provide charts and graphs depicting building activity.

FIG. 2B depicts a graphical user interface 200 including an exemplary building automation bot client application 210, according to one embodiment. The bot client application 210 may include computer-executable instructions for receiving a user input via an input field 212, transmitting the user input to a remote bot via a computer network (e.g., via the network 106 of FIG. 1 ) and/or for receiving and displaying output from the remote bot. In some embodiments, the bot client application 210 includes a plurality of channels 214. For example, the depicted embodiment includes a channel 214-A and a channel 214-B. In the depicted embodiment, the channel 214-A receives messages from the bot that are of a general building management nature, and the channel 214-B receives messages related to electronic vehicle parking. Commands typed by the user into the input field 212 may be automatically processed and analyzed by the remote bot. The input field 212 may accept commands that are formatted in a particular grammar, or syntax. For example, a command may be provided that includes one or more command arguments, as discussed below. Output received from the remote bot may be displayed in an output area 216.

Exemplary Automated Check-In (E-Receptionist) Embodiment

In addition to administrative features, such as the application 202, and bot features, as depicted in the application 210, the present techniques may include logging features. For example, FIG. 3A depicts a GUI 300 including a table 302 of logging information corresponding to building visitors. Each row of the table 302 includes a visitor full name, a corresponding visited full name, a visitor status, a visitor check in time, and a visitor image. The cells of the table 300 may be links, such that a user may access a link within a column (e.g., the status column) to receive/retrieve information or cause an action to occur. For example, the user may click a user image to view a larger image. The e-reception module 162 may generate the table 302 by receiving/retrieving information from a database (e.g., the database 190).

FIG. 3B depicts an exemplary GUI 310 including a login prompt 312. The e-reception module 162 may display the GUI 310 and the login prompt 312 in a kiosk computer of a building lobby, for example. In response to a user accessing the login prompt 312, the e-reception module 162 may display a visitor registration GUI 320, as shown in FIG. 3C. The visitor registration GUI 320 includes user registration information 322, action buttons 324, and a photo 326. The user may enter registration information 322 via an input device (e.g., the input device 140 of FIG. 1 ). The user may access the action buttons 324 to cause a camera to capture and/or recapture the image 326 of the user. For example, FIG. 3D depicts an exemplary visitor registration GUI 330, that may correspond to the visitor registration GUI 320.

In the visitor registration GUI 330, the user has entered information and retaken the photo 326. In particular, the user has entered the name of a visited person. In FIG. 3E, a visitor registration GUI 340 displays the visitor's entered information, along with two photos captured by the visitor. The visitor registration GUI 340 further includes a progress bar 328 that may be displayed while the visitor registration GUI 340 processes the input provided by the user.

Specifically, in some embodiments, the GUI 310, the GUI 320, the GUI 330 and/or the GUI 340 are generated by the GUI module 122 of FIG. 1 . Further, each may include instructions for submitting information via the network 106 to the reception module 164 of FIG. 1 . The reception module 164 may include instructions for processing the input. For example, the reception module 164 may store the input in the database 190, pass the data to another module (e.g., the bot module 162), or take another action (e.g., transmit a notification to the visited person).

FIG. 3F depicts an exemplary GUI 350 for displaying notifications to a visited user. The GUI 350 includes an application 352 that may correspond to the bot client application 210 of FIG. 2B, in some embodiments. The application 352 includes an output area 354 and a channel list 356. The application 352 may receive one or more messages (e.g., a notification) in one or more channels concurrently. For example, the application 352 may include a plurality of concurrent HTTP connections to a remote server (e.g., the server 104). The application 352 may include instructions for displaying visual feedback (e.g., a text color, an animation, etc.) in association with display of the channels. The visual feedback may indicate when a message is received into one of the respective channels.

In some embodiments, the application 352 may display the one or messages in the output area 354. For example, as depicted in FIG. 3F, the application 352 may display a visitor arrival notification 358. The visitor arrival notification 358 may include information corresponding to that entered by the visitor (e.g., into the visitor registration GUI 320), wherein the information identifies the visitor to the user of the GUI 350 (i.e., the person visited). The visitor arrival notification 358 may further include one or more response inputs 360, wherein the user may access the response inputs 360 to transmit a predetermined message to the visitor and/or to view additional information about the visitor.

The GUI 350 may include instructions that are executed (e.g., by the GUI module 122 of FIG. 1 ) in response to the user accessing one or more of the response inputs 360. For example, the user may access a five minutes out button of the response inputs 360. In response to the user's access, the GUI module 122 may cause a message request to be transmitted to a notifications module in the server 104 (not depicted). The message request may include the information of the visitor, including the visitor email address, as well as a message corresponding to the accessed button or other GUI element. The notifications module may cause a notification to be transmitted to the visitor.

FIG. 3G depicts an exemplary notification GUI 370 including an email message 372 corresponding to a notification. The email message 372 includes a message notifying the visitor corresponding to the response input 360 accessed by the visited user via the GUI 350. In some embodiments, other modes of communicating the notification may be used (e.g., a push message, a text message, an automated telephone call, etc.). It should also be appreciated that the response inputs 360 may include other predetermined notification messages and/or more complex functionality (e.g., the user may be prompted to enter a custom message to send to the visitor). In some embodiments, the user may reject the visitor. In still further embodiments, the user may enter bot commands into an input field 362. For example, the user may enter a command such as “notify visitor meet at the coffee shop.” In that case, the GUI 370 may include instructions for analyzing the user's bot command (e.g., via the bot client module 124 of FIG. 1 ). The bot client module 124 may determine that “notify visitor” corresponds to a notification command, and that the remaining bot command (i.e., “meet at the coffee shop”) is an argument for transmission as a message payload to the visiting user. The “notify” command may be sent by a preconfigured default notification route.

FIG. 3H depicts a GUI 380 including a table 382 of logging information corresponding to signed in building visitors. Continuing the previous example, the visited user may greet the visitor (e.g., at a building lobby). The visited user may use one or more credentials (e.g., a personal identification number (PIN), a biometric credential, a radio frequency identification (RFID) tag, etc.) to identify the visited user. The visited user may self-identify by entering the one or more credentials, for example, into a client computing device and authorizing the visitor based on the personal recognizance of the visited user.

Once the visited user has authorized the visiting user, the client computing device (e.g., the client computing device 104) may transmit a request including the identification of the visitor and the identity of the authorizing visited user to a remote computing device (e.g., the server 104). The server 104 may generate a timestamp and insert a sign in record into the table 382. The sign in records provide administrators with an audit trail that advantageously allows the present techniques to automatically track the entry of visitors. The table may be, for example, a table stored in the database 190 of FIG. 1 . In some embodiments, the reception module 164 of the server 104 may receive the request transmitted by the client computing device 102. The reception module 164 may cause another module (e.g., the badging module 166) to transmit a response to the client computing device 102. The response may include an authorization token and/or message notifying the visited user that the visitor may enter the premises of the building.

Exemplary Security Badging Embodiment

Security badging may be used to track the movements of visitors and building occupants within the building. For example, FIG. 4A depicts a GUI 400 including a monitoring application 402. The monitoring application 402 includes an output area 404 and an input field 406. The badging module 166 may monitor one or more aspects of the building via one or more sensors (e.g., via an RFID sensor as discussed above, via a camera, etc.). The badging module 166 may include a machine learning model trained to detect one or both of (i) motion, and (ii) objects (e.g., a human). When the badging module 166 detects motion, the badging module 166 may generate a notification and transmit the notification for display in the output area 404. As depicted, the notification may include a message including the category of detected object (e.g., Person), the location of the object (e.g., Zone 1 Door) and/or a live photograph or a file photograph. The badging module 166 may identify a file photograph by comparing a live photograph (i.e., a photograph captured in response to motion detection) to one or more lists of photographs. The lists of photographs may be searched in a priority order. For example, the badging module 166 may search current visitor photographs, followed by current employee photographs. In some embodiments, the badging module 166 may be configured to search for employees followed by consultants/contractors, as denoted by personnel records in the database 190.

In some embodiments, the present techniques may implement multi-factor badging authentication. Specifically, a badging client device may be configured to display a user registration GUI, such as the GUI 410 of FIG. 4B. Such a badging client device may be an instance of the client device 102, for example, and may be located in a secured location to prevent unauthorized access (e.g., in a building security office). When a new person (e.g., a building occupant/employee) needs access to the building, either temporarily or more permanently, the new person may enter information into an application 412 of the GUI 410. The application 412 may include instructions for displaying a form to collect user information, such as the full name of the user. The user may provide a PIN and may be assigned a physical RFID tag having an RFID number. The application 412 may include further instructions for collecting a profile photograph (e.g., a photograph of the user's face). Each of the pieces of information collected by the application 412 may be a respective factor used to identify the user. Some or all of the factors may be required to successfully identify the user, depending on the embodiment.

FIG. 4C depicts an exemplary computing environment 420 for implementing multi-factor authentication. The environment 420 includes a power supply 422, a lock relay 424, a door lock 426, a breadboard 428, a single-board computer 430, an LCD display 432, a camera 434, and an RFID tag 436. The power supply 422 may be a 12-volt power supply that operates in AC/DC mode, and supplies electric power to the other components of the computing environment 420. The lock relay may respond to electrical signals from the breadboard 428 and lock or unlock a locking rod or latch within the door lock 426 to an open or closed position. The breadboard 428 may be a general purpose input/output (GPIO) board, in some embodiments. The RFID tag 436 may be accessible via USB in some embodiments. The door lock 426 may include an electric door strike, in some embodiments.

The single-board computer 430 may be, for example, a Raspberry Pi computer. The single-board computer 430 may correspond to the client computing device 102, in some embodiments, and may include instructions for reading a PIN of a user and/or for reading information from the RFID tag 436. The single-board computer 430 may include instructions for capturing one or more images via the camera 434. The camera 434 may be any suitable wired and/or wireless (e.g., networked) camera, and may capture images in the visible spectrum and/or infrared images.

In an embodiment, the server 104 may implement the single-board computer 430. In the case, the badging module 166 may include instructions for capturing a PIN, a photograph of a person, and/or an RFID tag/token (e.g., and RFID tag corresponding to the RFID tag 436). As depicted in FIG. 4D, to authenticate a user, the badging module 166 may request (e.g., via a GUI 440) that the user present the RFID tag. The badging module 166 may read a unique identifier (UID) from the RFID tag, which may act in a passive or active mode. The badging module 166 may compare the UID to a list of stored UIDs (e.g., by querying the database 190). When the UID matches one or more of the stored UIDs, the badging module 166 may return a Boolean value corresponding to a match or no match. The UID search may be a binary search, in some embodiments.

In some embodiments, once the badging module 166 determines whether the UID matches a stored UID, the badging module 166 may proceed to analyze a second factor. For example, in some embodiments, the badging module receives/retrieves an identifying factor from the user (e.g., a PIN). For example FIG. 4E depicts an exemplary GUI 450 including a PIN application 452 for receiving/retrieving a PIN from the user. In some embodiments, the application 452 corresponds to a set of computer-executable instructions stored in one of the modules 120, such as the GUI module 122. In some embodiments, the application 452 requests a PIN in response to the user's successful supply of a first factor (e.g., an RFID matching a stored RFID corresponding to the user). In some embodiments, the application 452 may display the number of digits entered by the user, as depicted in FIG. 4F.

The badging module 166 may collect a third factor from the user in some embodiments. For example, once the user has supplied a known RFID tag and a PIN corresponding to the known RFID tag, the badging module 166 may request a photograph of the user, as depicted in GUI 460 of FIG. 4G.

The GUI 460 may include an application 462 that instructs the user to look at a camera. For example, the client 102 may display the application 462 in the output device 142 of the client 102. The application 462 may display an image capture countdown. A camera accessible to the client 102 (e.g., the input device 140) may capture a photograph of the user and display a message to that effect in the output device 142.

The client 102 may transmit the captured photograph of the user and an indication of the user's identity (e.g., a username, email address, or other unique identifier) to a remote computing device for analysis, such as the server 104. A module of the server 104 (e.g., the ML module 178) may retrieve a stored photo of the user using the unique identifier, and compare the captured photograph of the user to the stored photograph to predict a likelihood that the captured photograph and the stored photograph are of the same person. In some embodiments, the ML module 178 may analyze the photograph of the user using a trained facial recognition model. When the likelihood is above a predetermined threshold, the ML module 178 may indicate that the photos are a match.

The match of the two photos may be used as a third authenticating factor in some embodiments. In such embodiments, the successfully-authenticated user will have provided 1) physical access to a device provided by the building management (e.g., the RFID tag); 2) information known only to the user (e.g., the PIN); and 3) information indicating the physical identity of the user (e.g., the photograph). Thus, the present techniques provide a very strong authentication guarantee, in that the user has provided multiple factors. An attacker may be able to forge or steal one, or possibly two of the factors, but achieving simultaneous forgery/theft of all three factors may be much more difficult.

Exemplary Electric Vehicle Spot Finder Embodiment

FIG. 5A depicts an exemplary help menu GUI 500 including a building management application 502. The application 502 includes an input field 504 and an output area 506 that may correspond, respectively, to the input field 406 and the output area 404 of FIG. 4A, for example. The input field 504 may accept one or more input commands and/or input parameters. The input commands and/or input parameters may be received by a set of computer-executable instructions executing in a memory, such as the GUI module 122 of FIG. 1 . For example, in the depicted example of FIG. 5A, the user has typed the command 508. The command 508 is received by the GUI module 122, in an embodiment. The GUI module 122 may process the command, and output a message 510 into the output area 506. It should be appreciated that in the depicted example, the user is requesting help.

The help output may be stored as a static message, and therefore, the GUI module 122 may display the help output in the output area 506 by retrieving the static message from a memory (e.g., the memory 113, the database 190, etc.). However, for some commands that include dynamic output, and/or output that requires further analysis/computation, the GUI module 122 may cause one or more modules of a client computing device (e.g., the computing device 102) to process the user command to generate the output, and/or transmit one or more requests, and/or receive one or more responses to such transmitted requests via a network and/or a remote computing device (e.g., by accessing the server 104 over the network 106). For example, the bot client module 124 may process commands and/or parameters entered by the user.

The message 510 depicts exemplary commands available to the user (e.g., a building automation manager, an employee, etc.). In the depicted embodiment, the commands may be configured to manage aspects of electric vehicle parking, as shown in further detail in FIG. 5B.

FIG. 5B depicts an exemplary GUI 520 that may correspond to the GUI 500. The GUI 520 may include an application 522 that has an input field 524 and an output area 526. The application 522 includes a user command (e.g., find EV locations) parameterized by a distance (e.g., 4 miles). The command output 530 includes a list of locations within a radius of the distance, including descriptive information about each of the locations. The descriptive information may include a name, an address, a respective list of port types, and a link allowing the user to access a map of the location. When the user accesses the link, the application 522 may be configured to display map, as depicted in FIG. 5C.

FIG. 5C depicts a GUI 530 including a map 532. The map 532 includes a map description 534 corresponding to one of the list of locations depicted in FIG. 5B. The map includes a route 536 depicting a route from the user's current location to the one of the list of locations. Therefore, by using commands, the user may find suitable parking locations that are nearby the user's current location. In some embodiments, the user's current location is fixed, such as when the user is within a building. In some embodiments, the GUI module 122 may access a GPS module of the client 102 (not depicted) to determine the user's location. In still further embodiments, web-based technologies (e.g., Geolocation) may be used to determine the location of the user based on the location of the user's device.

The present techniques may be used to identify an open/available EV parking spot/space. For example, the user may enter a command/instruction into the input field (e.g., “Check for open spaces”) as shown in FIG. 5A. The GUI module 122 may be configured to analyze an image of a physical area to identify one or more open space. For example, FIG. 5E depicts a parking area 550 including one or more parking spaces each of which may contain a respective vehicle. The parking area 550 may correspond to a photograph of a parking lot, in some embodiments. In some embodiments, the GUI module 122 may transmit the user command to a module of a remote computing device, such as the parking module 168 of FIG. 1 . The parking module 168 may further analyze the command, for example, via the ML module 178.

The parking spaces in the parking area 550 may be marked or unmarked, wherein the presence of a marking indicates a spot type. For example, the parking spaces may include one or more EV parking spaces 552, denoted by the presence of an EV marking. The marking may be applied to the parking space using paint, for example. An accessible, disabled or handicap parking space may include another marking type. The EV parking spaces may each include a charging port 554.

In some embodiments, the charging port 554 may be an instance of the client 104. For example, the charging port 554 may be communicatively coupled to the network 106. The charging port may include an input device 140 and an output device 142, for example. The charging port 554 may provide electric power to EVs for charging, and may include a data link communication to/from EVs.

As noted, the present techniques may assist a user by identifying an open/available EV parking space. Identifying the open/available EV parking spaces may include the application of computer vision techniques and/or machine learning. For example, the ML module 178 of FIG. 1 may be configured to analyze an image of a physical area (e.g., the parking area 550) using one or more trained ML model. The image of the physical area may be captured by a camera communicatively coupled to the client 102 or the server 104, for example. For example, in an embodiment, the client 104 may be a camera capture device stationed in a parking area (e.g., the parking area 550).

The camera may correspond to the camera 434, in some embodiments. The camera may be implemented as a using a Cisco Meraki Cloud Managed Security Cameras, in some embodiments. The camera may be communicatively coupled to the network 106, and thus, images captured by the camera may be analyzed by the one or more trained ML model of FIG. 1 . The one or more ML model may be trained by the ML model 178 analyzing labeled images. The one or more ML model may be operated by the ML module 178 of Figure and/or by the parking module 168.

In some embodiments, an existing library/programming language may be used for implementation, such as Python/OpenCV. A pre-trained network such as the Resnet 50 neural network may be implemented. Google TensorFlow may be used to implement a particular ML technique. In some embodiments, a convolutional neural network (CNN) may be trained on many images to recognize/identify vehicles. For example, a pre-trained deep neural network may be used to identify only electric vehicles in the parking area 550. Other supporting technologies that may be used to implement aspects of the present techniques in some embodiments include xAPI Phillips-E, Amazon Alexa, AWS Rekognition, Dropbox file storage application programming interface (API), Google Dialogflow API, Meraki API, JavaScript Webcam API NodeMailer email API Open Charge Map API, Phillips HUE API and Cisco Webex Teams API. Further technologies that may be used to implement the present techniques include Python, JavaScript, Tkinter, jQuery and Node.js.

The user may enter a command that accesses the EV parking space identifying capabilities of the present techniques. For example, FIG. 5F depicts a GUI 570 including an application 572. The application 572 includes an input field 574. The application 572 depicts an example input command 576. The input command 576 may be output, or echoed, to the application 572 via an output device, such as the output device 142. The input command 576 may be received via the GUI module 122 and additionally/alternatively transmitted to a command processing module, such as the bot client 124 and/or the parking module 168. The parking module 168 may receive/retrieve a real-time image of the parking spaces in the parking area 550. The parking module 168 may analyze the image of the physical area to identify one or more vehicles in the parking area 550. The parking module 168 may further identify one or more spaces. Identifying the vehicles and spaces may include analyzing the image using one or more respective trained ML models.

For example, the parking module 168 may analyze the image using one or more trained ML models. The ML models may analyze contours of the image. The parking module 168 identify one or more contours of the image and may iterate over the contours. The parking module 168 may use a Douglas-Peucker algorithm to generate an approximate contour for the image, wherein the approximation identifies one or more parking spots 556 by identifying bounding boxes corresponding to an object of interest (e.g., a parking space, a vehicle, etc.). The one or more ML models may store a set of pixel coordinates (e.g., x1, y1, x2, y2) defining each bounding box relative to the image.

In some embodiments, a vehicle detection ML model may identify a list of vehicles using a vehicle object detection machine learning model. The parking module 168 may receive a set of pixel coordinates for each identified vehicle from the ML model, and store the pixel coordinates (e.g., in the database 190). The vehicle object detection ML model may be a retina net and may identify vehicles such as passenger cars, motorcycles, and/or trucks, as depicted in FIG. 5E. The output of the vehicle object detection ML model may include a vehicle type flag denoting the vehicle type as a passenger vehicle, motorcycle, truck, etc. The vehicle object detection ML model may identify the one or more vehicles in the parking area 550 as discussed above by drawing one or more vehicle bounding box 558 around the identified vehicles in the image. A spot detection ML model may identify a list of EV spots. The spot detection ML model may store pixel coordinates for each of the identified EV spots.

The parking module 168 may compute the area of spot bounding boxes 556 and the area of vehicle bounding boxes 558, and compare the respective areas, to determine respective overlap. When there is a predetermined amount of overlap (e.g., less than 40%), the parking module 168 may determine that the spot is unavailable. Of course, the overlap threshold may be adjusted to increase the sensitivity of the modeling. When there is not overlap, the parking module 168 may determine that the spot is available. The status of the respective parking spots within the parking area 550 may be stored in the database 190, for example. The historical status of the parking spots may be stored and queried over time. The parking module 168 may compute the width and height of the overlap of each vehicle bounding box with each spot bounding box by computing the intersection of the boxes.

In some embodiments, as depicted in FIG. 5F, the bot may notify that user via the application 572 that no spots are available. In such cases, the user may be prompted to query nearby EV parking locations with particular port types as shown in FIG. 5D, sorted by least distance, and filtered by port type. Specifically, the user command 542 includes a user-specified port type 544 and a user-specified proximity 546. In this way, the present techniques advantageously improve the efficiency of locating geographically proximate charging stations 548 that are limited to an appropriate port type for the user's vehicle, and are within a specified distance. In some embodiments, the charging stations 548 may be automatically displayed to the user in response to detection by the present techniques of a full local parking lot.

The spot identification techniques discussed with respect to FIG. 5E may be used periodically and/or upon user demand. For example, FIG. 5G depicts an exemplary GUI 580. The GUI 580 includes an application 582 that may correspond to the building automation bot client application 210, in some embodiments. The application 582 may include an input area 584 for receiving user commands and an exemplary command 586. The application 582 may include a first exemplary output message 588-A notifying the user, in response to the input command 586, that a spot is available in the parking area 550. The spot identification may function as discussed with respect to FIG. 5F. In some embodiments, the open spot may be identified visually by way of color highlighting, font styling (e.g., bold/italicized text), animation or other suitable means.

The spot identification techniques discussed with respect to FIG. 5E may be periodically executed (e.g., once every five minutes) and when the status of the parking area 550 changes, the parking module 168 may generate an output and transmit the output to the user. For example, the parking module 168 may periodically call the trained spot identification model. The model may generate output indicating that a spot is available, and/or that an available spot is no longer available. As depicted in FIG. 5G, the model may generate a second exemplary output message 588-B notifying the user that the spot is no longer available in the parking area 550.

Exemplary Room Presence Embodiment

In an embodiment, the present techniques may be used to manage aspects of room presence. For example, FIG. 6A depicts a GUI 600 including a table 602 of room presence information. The table 602 may include one or more rows, each corresponding to a room within the building/buildings. Each row may include room information such as a name, a Boolean flag indicating the presence of people in the room, a count of people, a call status, a map link, etc. A user may access information corresponding to each room by accessing the map link. The GUI 600 may include instructions that cause the GUI 600 to display additional information in response to user input. For example, when the user accesses the map link within a row of the table 602, the GUI 600 may display a detailed map of the room corresponding to the accessed row.

FIG. 6B depicts a room layout GUI 610 that includes a detailed real-time room view 612. The room view 612 includes a room diagram 614 including a person indicator 616. When the user accesses the person indicator 616, the GUI 610 may display a person identifier 618 that includes the identity of the corresponding person. The GUI 610 may receive the name of the room and/or a unique room identifier as a parameter when the user accesses the map link of the GUI 600.

Exemplary Lighting Control Embodiment

The present techniques may be used to control aspects of room lighting. For example, FIG. 7A depicts an exemplary GUI 700 including lighting control access 700. The user may access the lighting control access 700, via a secured control panel that requires authentication. For example, the control panel may correspond to the input device 140, and the lighting control access may be provided in a stationary access panel implemented as an instance of the client 104 (e.g., a panel affixed to a wall within the building).

The user may control the status of lighting within the building using the lighting control. For example, FIG. 7B depicts a lighting control GUI 720 that includes a light control application 724. The lighting control application 724 may include one or more lighting control buttons that when accessed by the user (e.g., via a mouse click, a touch screen panel, etc.) cause one or more electric light within a respective room to toggle on or off. For example, the lighting control buttons may each correspond to one of the rooms within the room presence information table 602.

In some embodiments, the lighting within the rooms may be implemented using smart lighting (e.g., Phillips Hue lighting). The lighting controls may be integrated into the bot system described above, such that in some embodiments, the bot client module 124 receives a user command (e.g., “turn on lights in SpaceX Room”), analyzes the command to identify that the command includes an instruction (“turn on lights”) and a parameter (“SpaceX Room”). The bot client module 124 may transmit the instruction and parameter to the lighting module 172 of the server 104. The lighting module 172 may include a set of computer-executable instructions for controlling lighting, and may perform an action to a particular set of lights based on the instruction and the parameter. The bot client module 124 may receive a response message from the lighting module 172 and display the response message to the user.

Exemplary Methods

FIG. 8 depicts a flow diagram of an example method 800 for classifying a vehicle parking space. The method 800 may include receiving, in an input field of an application of a graphical user interface, a user command including an instruction to locate an available vehicle parking space (block 802). For example, the user command may be entered by a user into the input device 140 of FIG. 1 . The GUI module 122 may include instructions for receiving the user command. The GUI module 122 may pass the user command to the bot module 124. The bot module 124 may process the user command to extract one or more instructions including one or more parameters. The bot module 124 may transmit the user command via the network 106 to the bot module 162. The bot module 162 may transmit a response to the user for display in the output device 142 by the GUI module 122. The application may be a building management bot application, such as the application 202 depicted in FIG. 2A. The input field of the application may correspond to the input field 574 of FIG. 5F, for example. The user may type a command such as the command 576, for example.

Concurrently, the bot module 124 may analyze the user command to determine the user's intent. A trained intent processing ML model may be used in some embodiments to determine the user intent. Based on the user's intent and/or the contents of the user command, the bot module 162 may access one or more of the modules 160. For example, the bot module 162 may pass the user command and/or one or more arguments of the user command to the ML module 178 for processing. For example, when the user command relates to finding an available vehicle parking space, the ML module 178 may analyze an image of a parking area using one or more trained ML model to identify one or more available parking spaces.

Specifically, the method 800 may include analyzing an image of a parking area (e.g., the parking area 550 of FIG. 5E) using a spot detection machine learning model to identify one or more parking space bounding boxes, each corresponding to a respective set of pixels (block 804). In some embodiments, the spot detection machine learning model includes analyzing the image of the parking area to identify one or more contours relating to a painted region of the parking space.

The method 800 may further include analyzing the image of the parking area using a vehicle detection machine learning model to identify one or more vehicle bounding boxes, each corresponding to a respective set of pixels (block 806). In some embodiments, the vehicle detection machine learning model may include analyzing the image of the parking area using a retina net configured to identify a vehicle type. The method 800 may include comparing each identified parking space bounding box to each vehicle bounding box by calculating the area of intersection of each parking space bounding box to each vehicle bounding box (block 808).

The method 800 may include, when the area of the intersection does not exceed a predetermined percentage value, classifying the parking space corresponding to the parking space bounding box as available (block 810). For example, the predetermined percentage value is 40%. The ML model may output an integer value from 0 to the number of spaces in the parking area, reflecting the number of available spaces. The ML model may also output an image of each space, in some embodiments, as depicted in exemplary output message 588-A and exemplary output message 588-B of FIG. 5G.

Once the ML module 178 has analyzed the user command, the bot module 162 may receive output of the ML module 178. The bot module 162 may transmit a response to the user. For example, the method 800 may include transmitting a notification to a client computing device of a user, the notification including an identification of the available parking space, and displaying the notification in an output area of a bot graphical user interface of the client computing device, as depicted in FIG. 5G. It should be appreciated that the method 800 may include transmitting a notification that the lot is or has become full, as depicted respectively in FIG. 5F and FIG. 5G. When the output of the ML module 178 indicates no available parking spaces, the method 800 may include displaying a list of nearby parking locations, such as the command output 530 of FIG. 5B.

Additional Considerations

The following considerations also apply to the foregoing discussion. Throughout this specification, plural instances may implement operations or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.

It should also be understood that, unless a term is expressly defined in this patent using the sentence “As used herein, the term” “is hereby defined to mean . . . ” or a similar sentence, there is no intent to limit the meaning of that term, either expressly or by implication, beyond its plain or ordinary meaning, and such term should not be interpreted to be limited in scope based on any statement made in any section of this patent (other than the language of the claims). To the extent that any term recited in the claims at the end of this patent is referred to in this patent in a manner consistent with a single meaning, that is done for sake of clarity only so as to not confuse the reader, and it is not intended that such claim term be limited, by implication or otherwise, to that single meaning. Finally, unless a claim element is defined by reciting the word “means” and a function without the recital of any structure, it is not intended that the scope of any claim element be interpreted based on the application of 35 U.S.C. § 112(f).

Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information.

As used herein any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.

As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).

In addition, use of “a” or “an” is employed to describe elements and components of the embodiments herein. This is done merely for convenience and to give a general sense of the invention. This description should be read to include one or at least one and the singular also includes the plural unless it is obvious that it is meant otherwise.

Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs for implementing the concepts disclosed herein, through the principles disclosed herein. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, changes and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope defined in the appended claims. 

What is claimed:
 1. A computing system for classifying a vehicle parking space, comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the system to: generate a spot detection machine learning model trained to identify a parking space bounding box corresponding to the vehicle parking space, the spot detection machine learning model including a contour approximation algorithm; generate, by analyzing a plurality of labeled images, an artificial neural network trained to identify a vehicle bounding box; analyze an image of a parking area using the trained spot detection machine learning model to identify the parking space bounding box; analyze the image of the parking area using the trained vehicle detection machine learning model to identify the vehicle bounding box; and classify the vehicle parking space as available when an area of intersection of the parking space bounding box and the vehicle bounding box does not exceed a predetermined value.
 2. The computing system of claim 1, wherein the vehicle parking space is an electric vehicle parking space.
 3. The computing system of claim 1, the memory storing further instructions that, when executed by the one or more processors, cause the server to: capture the image of the parking area using a camera capture device stationed in the parking area.
 4. The computing system of claim 1, the memory storing further instructions that, when executed by the one or more processors, cause the server to: analyze the image of the parking area to identify one or more contours relating to a painted region of the parking space.
 5. The computing system of claim 1, the memory storing further instructions that, when executed by the one or more processors, cause the server to: analyze the image of the parking area using a retina net configured to identify a vehicle type.
 6. The computing system of claim 1, the memory storing further instructions that, when executed by the one or more processors, cause the server to: transmit a notification to a client computing device of a user, the notification including an identification of the available parking space, and display the notification in an output area of a bot graphical user interface of the client computing device.
 7. The computing system of claim 1, wherein the predetermined percentage value is 40%.
 8. A computer-implemented method for classifying a vehicle parking space, comprising: analyzing an image of a parking area using a spot detection machine learning model to identify a parking space bounding box; analyzing the image of the parking area using a vehicle detection machine learning model to identify a vehicle bounding box; and classifying the vehicle parking space as available when an area of intersection of the parking space bounding box and the vehicle bounding box does not exceed a predetermined value.
 9. The computer-implemented method of claim 8, wherein the vehicle parking space is an electric vehicle parking space.
 10. The computer-implemented method of claim 8, further comprising: analyzing the image of the parking area to identify one or more contours relating to a painted region of the parking space.
 11. The computer-implemented method of claim 8, further comprising: analyzing the image of the parking area using a retina net configured to identify a vehicle type.
 12. The computer-implemented method of claim 8, further comprising: transmit a notification to a client computing device of a user, the notification including an identification of the available parking space, and display the notification in an output area of a bot graphical user interface of the client computing device.
 13. The computer-implemented method of claim 8, further comprising: capturing the image of the parking area using a camera capture device stationed in the parking area.
 14. The computer-implemented method of claim 8, wherein the predetermined percentage value is 40%.
 15. A computer-implemented method for training a plurality of machine learning models used for classifying a vehicle parking space, comprising: generating a spot detection machine learning model trained to identify a parking space bounding box corresponding to the vehicle parking space, the spot detection machine learning model including a contour approximation algorithm; and generating, by analyzing a plurality of labeled images, an artificial neural network trained to identify a vehicle bounding box.
 16. The computer-implemented method of claim 15, wherein the artificial neural network trained to identify the vehicle bounding box is implemented using a pre-trained neural network.
 17. The computer-implemented method of claim 15, wherein the artificial neural network is a convolutional neural network.
 18. The computer-implemented method of claim 15, wherein the artificial neural network trained is a deep neural network.
 19. The computer-implemented method of claim 15, further comprising: training the artificial neural network to identify only vehicles of a type.
 20. The computer-implemented method of claim 15, wherein the contour approximation algorithm is the Douglas-Peucker algorithm. 